가입 후 초대 링크를 공유하면 동영상 재생 및 초대 보상을 받을 수 있습니다.

Yuhan Luo
@_yuhanluo
synthetic data @fleet_ai // prev: eng & product @dagster, operations research @MITSloan
가입 February 2013
604 팔로잉 중    589 팬
i’ve often worried that pass@k = 0 tasks weren’t worth shipping, but this makes me feel better about making extremely hard tasks -- it seems like failed traces can still yield useful learning signals, even when there’s no good solution to copy also this reminds me of a similar attempt: RLTL;DR: failed attempt + verifier feedback -> generate TL;DR lesson -> train the "task to lesson" mapping (no separate teacher) HINT-SD: full failed attempt -- thru hindsight analysis --> select relevant actions + generate corrective feedback -> train that "trace to action" mapping at selected steps, with hindsight informed teacher predictions (the teacher that comes from the student)
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We let an agent self-improve by exploring and internalizing ”on this sort of task, keep this sort of thing in mind“ bits of self-feedback. It learned to solve tasks where the original policy failed 128 attempts in a row and GRPO flatlined. Paper: 🧵1/8
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